Applied Mathematics
Vol.4 No.8(2013), Article ID:35326,12 pages DOI:10.4236/am.2013.48153
Two-Sided First Exit Problem for Jump Diffusion Processes Having Jumps with a Mixture of Erlang Distribution*
School of Mathematical Sciences, Qufu Normal University, Qufu, China
Email: #wenyzhen@163.com, ccyin@mail.qfnu.edu.cn
Copyright © 2013 Yuzhen Wen, Chuancun Yin. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Received May 29, 2013; revised June 29, 2013; accepted July 7, 2013
Keywords: First Exit Time; Two-Sided Jumps; Jump Diffusion Process; Overshoot
ABSTRACT
In this paper, we consider the two-sided first exit problem for jump diffusion processes having jumps with rational Laplace transforms. We investigate the probabilistic property of conditional memorylessness, and drive the joint distribution of the first exit time from an interval and the overshoot over the boundary at the exit time.
1. Introduction
Consider the following jump diffusion process
(1.1)
where the constant is the starting point of
,
and
represent the drift and the volatility of the diffusion part, respectively,
is a standard Brownian motion with
,
is a Poisson process with rate
, and the jumps sizes
are assumed to be i.i.d. real valued random variables with common density
. Moreover, it is assumed that the random processes
,
and random variables
are mutually independent. In this paper we are interested in the density
of following type
(1.2)
where,
,
,
and that
,
for all
. Moreover,
Define to be the first exit time of
to two flat barriers
and
, i.e.
Recently, one-sided and two-sided first exit problems for processes with two-sided jumps have attracted a lot of attentions in applied probability (see [1-7]). For example, Perry and Stadje [1] studied two-sided first exit time for processes with two-sided exponential jumps; Kou and Wang [2] studied the one-sided first passage times for a jump diffusion process with exponential positive and negative jumps. Cai [3] investigated the first passage time of a hyper-exponential jump diffusion process. Cai et al. [4] discussed the first passage time to two barriers of a hyper-exponential jump diffusion process. Closed form expressions are obtained in Kadankova and Veraverbeke [5] for the integral transforms of the joint distribution of the first exit time from an interval and the value of the overshoot through boundaries at the exit time for the Poisson process with an exponential component. For some related works, see Perry et al. [8], Cai and Kou [9], Lewis and Mordecki [10] and the references therein.
Motivated by works mentioned above, the main objective of this paper is to study the first exit time of the process (1.1) with jump density (1.2) from an interval and the overshoot over the boundary at the exit time. In Section 2, we study the roots of the generalized Lundberg equation and conditional memory lessness. The main results of this paper are given in Section 3.
2. Preliminary Results
It is easy to see that the infinitesimal generator of is given by
for any twice continuously differentiable function and the Lévy exponent of
is given by
By analytic continuation, the function can be extended to the complex plane except at finitely many poles. In the following, we consider the resulting extension
of
, i.e.,
Let us denote and
.
In [11], Kuznetsov has studied the roots of the equation. However, for this particular Lévy process
, we will give another simple proof for the roots of this equation.
Lemma 2.1. For fix, the generalized CramérLundberg equation
has complex roots
with
for
and
with
for
.
Proof. Let
Firstly, we prove that for given,
has
roots with negative real parts. Set
with
where
is an arbitrary positive constant. Applying Rouchés theorem on the semi-circle
, consisting of the imaginary axis running from
to
and with radius
running clockwise from
to
. We let
and denote by
the limiting semi-circle. It is known that both
and
are analytic in
. We want to show that
Notice that for
, and
is bounded for
. Hence, for
,
on the boundary of the half circle in. For
, we have
(see Lewis and Mordecki [10]). On the other hand,
Thus we have. Since
has
roots with negative real parts, so equation
has
roots with negative real parts. Similarly, we can prove
has
roots with positive real parts.
In the rest of this paper, we assume all the roots of equation are distinct and denote
,
for notational simplicity, and denote
(or
in the sequel) representing the expectation (or probability) when
starts from
. We denote a sequence of events
,
= {
:
crosses
at time
by the
th phase of
th positive jump whose parameter is
},
= {
:
crosses
at time
by the
th phase of
th negative jump whose parameter is
}
for,
,
,
,
and
.
Theorem 2.2. For any, we have
(2.1)
(2.2)
Furthermore, conditional on
, the stopping time
is independent of the overshoot
(the undershoot
). More precisely, for any
, we have
(2.3)
(2.4)
Proof. Firstly, we prove (2.1) and (2.3). It suffices to show
(2.5)
since (2.1) can be obtained by letting in (2.5) and then dividing both sides of the resulting equation by
. It is known that an Erlang(n) random variable can be expressed as an independent sum of
exponential random variables with same parameters. Let
the
independent exponentially distributed random variables with parameter
. Denote by
the arrival times of the Poisson process
, and let
be the field generated by process
,
. It follows that
With, we have
Thus we have
(2.2) and (2.4) can be obtained similarly. This completes the proof.
The following results are immediate consequences of Theorem 2.2.
Corollary 2.3. For,
,
,
,
,
, we have
Corollary 2.4. For any, we have
where
for,
,
,
.
Corollary 2.5. For,
,
,
, we have
Remark 2.6. When,
, (2.1) and (2.3) reduce to Equations (8) and (9) of Cai [3], respectively.
3. Main Results
In this section, we study the distribution of the first exit problem to two barriers. We first define three vectors:
where
Let
Define a matrix .
Theorem 3.1. Consider any nonnegative measurable function such that
and
for
,
,
,
. For any
and
, we have
(3.1)
where satisfies
(3.2)
Moreover, when is a non-singular matrix,
is the unique solution of (3.2), i.e.,
(3.3)
Proof. By the law of total probability, we have
It follows from Corollary 2.4, for,
,
,
, we have
Combining these equations, we get
The expressions for,
,
and
can be determined as follows. Let
denote the set of functions
such that is twice continuously differentiable and bounded for
with
and
bounded for
. By applying Itô formula to the process
, we have that for
and
,
where is a martingale with
. Note that we have
as
.
For any, we can easily obtain from the above equation that
where the last term of the above equation is a mean-0 martingale. This implies that
(3.4)
By simple calculation, the function with
and
satisfies
for
. It follows from (3.4) that the process
is a martingale. Then
(3.5)
Setting for
and
for
in (3.5), we have the following linear equations:
and
Then the vector satisfies
and we have (3.1). If
is non-singular, we have
. This completes the proof.
Corollary 3.2. For any
, we have
(3.6)
where
and
Remark 3.3. When,
, (3.1) and (3.6) reduce to equation (6) and (15) of [4], respectively.
From Theorem 3.1, choosing to be
,
,
,
,
,
and
respectively, we can obtain the following corollaries.
Corollary 3.4. 1) For any, we have
(3.7)
where
is determined by the linear system. Here
2) For any, we have
(3.8)
where
is determined by the linear system. Here
Corollary 3.5. 1) For and any
,
, we have
(3.9)
where
is determined by the linear system. Here
2) For and any
,
, we have
(3.10)
where
is determined by the linear system. Here
Note that the difference of
and
is exactly
. Thus we obtain the following results.
Corollary 3.6. 1) For, and for any
, we have
(3.11)
where
is determined by the linear system. Here
2) For and any
,
, we have
(3.12)
where
is determined by the linear system. Here
To end the paper, we give an example.
Example 3.7. When,
and
, the equation
has
real roots:
,
,
and
. Let
Denote by
Then we have
where
We define (
,
,
) and
(
,
,
) as follows: let
(
,
,
) be obtained from
(
,
,
) by changing
to
in
(
,
); let
(
,
,
) be obtained from
(
,
,
) by changing
to
in
(
,
,
).
• If, then we have
where
•
• If, then we have
where
•
• If, then we have
where
•
• If,
, then we have
•
• If,
, then we have
where
•
• If, then we have
where
•
• If, then we have
where
When, we have
Therefore, we have
These results are all consistent with that of Theorem 3.1 of Kou and Wang [2] for the one-sided exit problem of the doubly exponential jump diffusion process.
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NOTES
*This work was supported by the National Natural Science Foundation of China (No. 11171179) and Natural Science Foundation of Shandong Province (No. ZR2010AQ015).
#Corresponding author.